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  • Metro: Introduction to CSS 3 Grid Layout

    - by Stephen.Walther
    The purpose of this blog post is to provide you with a quick introduction to the new W3C CSS 3 Grid Layout standard. You can use CSS Grid Layout in Metro style applications written with JavaScript to lay out the content of an HTML page. CSS Grid Layout provides you with all of the benefits of using HTML tables for layout without requiring you to actually use any HTML table elements. Doing Page Layouts without Tables Back in the 1990’s, if you wanted to create a fancy website, then you would use HTML tables for layout. For example, if you wanted to create a standard three-column page layout then you would create an HTML table with three columns like this: <table height="100%"> <tr> <td valign="top" width="300px" bgcolor="red"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </td> <td valign="top" bgcolor="green"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </td> <td valign="top" width="300px" bgcolor="blue"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </td> </tr> </table> When the table above gets rendered out to a browser, you end up with the following three-column layout: The width of the left and right columns is fixed – the width of the middle column expands or contracts depending on the width of the browser. Sometime around the year 2005, everyone decided that using tables for layout was a bad idea. Instead of using tables for layout — it was collectively decided by the spirit of the Web — you should use Cascading Style Sheets instead. Why is using HTML tables for layout bad? Using tables for layout breaks the semantics of the TABLE element. A TABLE element should be used only for displaying tabular information such as train schedules or moon phases. Using tables for layout is bad for accessibility (The Web Content Accessibility Guidelines 1.0 is explicit about this) and using tables for layout is bad for separating content from layout (see http://CSSZenGarden.com). Post 2005, anyone who used HTML tables for layout were encouraged to hold their heads down in shame. That’s all well and good, but the problem with using CSS for layout is that it can be more difficult to work with CSS than HTML tables. For example, to achieve a standard three-column layout, you either need to use absolute positioning or floats. Here’s a three-column layout with floats: <style type="text/css"> #container { min-width: 800px; } #leftColumn { float: left; width: 300px; height: 100%; background-color:red; } #middleColumn { background-color:green; height: 100%; } #rightColumn { float: right; width: 300px; height: 100%; background-color:blue; } </style> <div id="container"> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> </div> The page above contains four DIV elements: a container DIV which contains a leftColumn, middleColumn, and rightColumn DIV. The leftColumn DIV element is floated to the left and the rightColumn DIV element is floated to the right. Notice that the rightColumn DIV appears in the page before the middleColumn DIV – this unintuitive ordering is necessary to get the floats to work correctly (see http://stackoverflow.com/questions/533607/css-three-column-layout-problem). The page above (almost) works with the most recent versions of most browsers. For example, you get the correct three-column layout in both Firefox and Chrome: And the layout mostly works with Internet Explorer 9 except for the fact that for some strange reason the min-width doesn’t work so when you shrink the width of your browser, you can get the following unwanted layout: Notice how the middle column (the green column) bleeds to the left and right. People have solved these issues with more complicated CSS. For example, see: http://matthewjamestaylor.com/blog/holy-grail-no-quirks-mode.htm But, at this point, no one could argue that using CSS is easier or more intuitive than tables. It takes work to get a layout with CSS and we know that we could achieve the same layout more easily using HTML tables. Using CSS Grid Layout CSS Grid Layout is a new W3C standard which provides you with all of the benefits of using HTML tables for layout without the disadvantage of using an HTML TABLE element. In other words, CSS Grid Layout enables you to perform table layouts using pure Cascading Style Sheets. The CSS Grid Layout standard is still in a “Working Draft” state (it is not finalized) and it is located here: http://www.w3.org/TR/css3-grid-layout/ The CSS Grid Layout standard is only supported by Internet Explorer 10 and there are no signs that any browser other than Internet Explorer will support this standard in the near future. This means that it is only practical to take advantage of CSS Grid Layout when building Metro style applications with JavaScript. Here’s how you can create a standard three-column layout using a CSS Grid Layout: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } </style> </head> <body> <div id="container"> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> </div> </body> </html> When the page above is rendered in Internet Explorer 10, you get a standard three-column layout: The page above contains four DIV elements: a container DIV which contains a leftColumn DIV, middleColumn DIV, and rightColumn DIV. The container DIV is set to Grid display mode with the following CSS rule: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } The display property is set to the value “-ms-grid”. This property causes the container DIV to lay out its child elements in a grid. (Notice that you use “-ms-grid” instead of “grid”. The “-ms-“ prefix is used because the CSS Grid Layout standard is still preliminary. This implementation only works with IE10 and it might change before the final release.) The grid columns and rows are defined with the “-ms-grid-columns” and “-ms-grid-rows” properties. The style rule above creates a grid with three columns and one row. The left and right columns are fixed sized at 300 pixels. The middle column sizes automatically depending on the remaining space available. The leftColumn, middleColumn, and rightColumn DIVs are positioned within the container grid element with the following CSS rules: #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } The “-ms-grid-column” property is used to specify the column associated with the element selected by the style sheet selector. The leftColumn DIV is positioned in the first grid column, the middleColumn DIV is positioned in the second grid column, and the rightColumn DIV is positioned in the third grid column. I find using CSS Grid Layout to be just as intuitive as using an HTML table for layout. You define your columns and rows and then you position different elements within these columns and rows. Very straightforward. Creating Multiple Columns and Rows In the previous section, we created a super simple three-column layout. This layout contained only a single row. In this section, let’s create a slightly more complicated layout which contains more than one row: The following page contains a header row, a content row, and a footer row. The content row contains three columns: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } #leftColumn { -ms-grid-column: 1; -ms-grid-row: 2; background-color:red; } #middleColumn { -ms-grid-column: 2; -ms-grid-row: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; -ms-grid-row: 2; background-color:blue; } #footer { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 3; background-color: orange; } </style> </head> <body> <div id="container"> <div id="header"> Header, Header, Header </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="footer"> Footer, Footer, Footer </div> </div> </body> </html> In the page above, the grid layout is created with the following rule which creates a grid with three rows and three columns: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } The header is created with the following rule: #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } The header is positioned in column 1 and row 1. Furthermore, notice that the “-ms-grid-column-span” property is used to span the header across three columns. CSS Grid Layout and Fractional Units When you use CSS Grid Layout, you can take advantage of fractional units. Fractional units provide you with an easy way of dividing up remaining space in a page. Imagine, for example, that you want to create a three-column page layout. You want the size of the first column to be fixed at 200 pixels and you want to divide the remaining space among the remaining three columns. The width of the second column is equal to the combined width of the third and fourth columns. The following CSS rule creates four columns with the desired widths: #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } The fr unit represents a fraction. The grid above contains four columns. The second column is two times the size (2fr) of the third (1fr) and fourth (1fr) columns. When you use the fractional unit, the remaining space is divided up using fractional amounts. Notice that the single row is set to a height of 1fr. The single grid row gobbles up the entire vertical space. Here’s the entire HTML page: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } #firstColumn { -ms-grid-column: 1; background-color:red; } #secondColumn { -ms-grid-column: 2; background-color:green; } #thirdColumn { -ms-grid-column: 3; background-color:blue; } #fourthColumn { -ms-grid-column: 4; background-color:orange; } </style> </head> <body> <div id="container"> <div id="firstColumn"> First Column, First Column, First Column </div> <div id="secondColumn"> Second Column, Second Column, Second Column </div> <div id="thirdColumn"> Third Column, Third Column, Third Column </div> <div id="fourthColumn"> Fourth Column, Fourth Column, Fourth Column </div> </div> </body> </html>   Summary There is more in the CSS 3 Grid Layout standard than discussed in this blog post. My goal was to describe the basics. If you want to learn more than you can read through the entire standard at http://www.w3.org/TR/css3-grid-layout/ In this blog post, I described some of the difficulties that you might encounter when attempting to replace HTML tables with Cascading Style Sheets when laying out a web page. I explained how you can take advantage of the CSS 3 Grid Layout standard to avoid these problems when building Metro style applications using JavaScript. CSS 3 Grid Layout provides you with all of the benefits of using HTML tables for laying out a page without requiring you to use HTML table elements.

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  • Metro: Introduction to CSS 3 Grid Layout

    - by Stephen.Walther
    The purpose of this blog post is to provide you with a quick introduction to the new W3C CSS 3 Grid Layout standard. You can use CSS Grid Layout in Metro style applications written with JavaScript to lay out the content of an HTML page. CSS Grid Layout provides you with all of the benefits of using HTML tables for layout without requiring you to actually use any HTML table elements. Doing Page Layouts without Tables Back in the 1990’s, if you wanted to create a fancy website, then you would use HTML tables for layout. For example, if you wanted to create a standard three-column page layout then you would create an HTML table with three columns like this: <table height="100%"> <tr> <td valign="top" width="300px" bgcolor="red"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </td> <td valign="top" bgcolor="green"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </td> <td valign="top" width="300px" bgcolor="blue"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </td> </tr> </table> When the table above gets rendered out to a browser, you end up with the following three-column layout: The width of the left and right columns is fixed – the width of the middle column expands or contracts depending on the width of the browser. Sometime around the year 2005, everyone decided that using tables for layout was a bad idea. Instead of using tables for layout — it was collectively decided by the spirit of the Web — you should use Cascading Style Sheets instead. Why is using HTML tables for layout bad? Using tables for layout breaks the semantics of the TABLE element. A TABLE element should be used only for displaying tabular information such as train schedules or moon phases. Using tables for layout is bad for accessibility (The Web Content Accessibility Guidelines 1.0 is explicit about this) and using tables for layout is bad for separating content from layout (see http://CSSZenGarden.com). Post 2005, anyone who used HTML tables for layout were encouraged to hold their heads down in shame. That’s all well and good, but the problem with using CSS for layout is that it can be more difficult to work with CSS than HTML tables. For example, to achieve a standard three-column layout, you either need to use absolute positioning or floats. Here’s a three-column layout with floats: <style type="text/css"> #container { min-width: 800px; } #leftColumn { float: left; width: 300px; height: 100%; background-color:red; } #middleColumn { background-color:green; height: 100%; } #rightColumn { float: right; width: 300px; height: 100%; background-color:blue; } </style> <div id="container"> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> </div> The page above contains four DIV elements: a container DIV which contains a leftColumn, middleColumn, and rightColumn DIV. The leftColumn DIV element is floated to the left and the rightColumn DIV element is floated to the right. Notice that the rightColumn DIV appears in the page before the middleColumn DIV – this unintuitive ordering is necessary to get the floats to work correctly (see http://stackoverflow.com/questions/533607/css-three-column-layout-problem). The page above (almost) works with the most recent versions of most browsers. For example, you get the correct three-column layout in both Firefox and Chrome: And the layout mostly works with Internet Explorer 9 except for the fact that for some strange reason the min-width doesn’t work so when you shrink the width of your browser, you can get the following unwanted layout: Notice how the middle column (the green column) bleeds to the left and right. People have solved these issues with more complicated CSS. For example, see: http://matthewjamestaylor.com/blog/holy-grail-no-quirks-mode.htm But, at this point, no one could argue that using CSS is easier or more intuitive than tables. It takes work to get a layout with CSS and we know that we could achieve the same layout more easily using HTML tables. Using CSS Grid Layout CSS Grid Layout is a new W3C standard which provides you with all of the benefits of using HTML tables for layout without the disadvantage of using an HTML TABLE element. In other words, CSS Grid Layout enables you to perform table layouts using pure Cascading Style Sheets. The CSS Grid Layout standard is still in a “Working Draft” state (it is not finalized) and it is located here: http://www.w3.org/TR/css3-grid-layout/ The CSS Grid Layout standard is only supported by Internet Explorer 10 and there are no signs that any browser other than Internet Explorer will support this standard in the near future. This means that it is only practical to take advantage of CSS Grid Layout when building Metro style applications with JavaScript. Here’s how you can create a standard three-column layout using a CSS Grid Layout: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } </style> </head> <body> <div id="container"> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> </div> </body> </html> When the page above is rendered in Internet Explorer 10, you get a standard three-column layout: The page above contains four DIV elements: a container DIV which contains a leftColumn DIV, middleColumn DIV, and rightColumn DIV. The container DIV is set to Grid display mode with the following CSS rule: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } The display property is set to the value “-ms-grid”. This property causes the container DIV to lay out its child elements in a grid. (Notice that you use “-ms-grid” instead of “grid”. The “-ms-“ prefix is used because the CSS Grid Layout standard is still preliminary. This implementation only works with IE10 and it might change before the final release.) The grid columns and rows are defined with the “-ms-grid-columns” and “-ms-grid-rows” properties. The style rule above creates a grid with three columns and one row. The left and right columns are fixed sized at 300 pixels. The middle column sizes automatically depending on the remaining space available. The leftColumn, middleColumn, and rightColumn DIVs are positioned within the container grid element with the following CSS rules: #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } The “-ms-grid-column” property is used to specify the column associated with the element selected by the style sheet selector. The leftColumn DIV is positioned in the first grid column, the middleColumn DIV is positioned in the second grid column, and the rightColumn DIV is positioned in the third grid column. I find using CSS Grid Layout to be just as intuitive as using an HTML table for layout. You define your columns and rows and then you position different elements within these columns and rows. Very straightforward. Creating Multiple Columns and Rows In the previous section, we created a super simple three-column layout. This layout contained only a single row. In this section, let’s create a slightly more complicated layout which contains more than one row: The following page contains a header row, a content row, and a footer row. The content row contains three columns: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } #leftColumn { -ms-grid-column: 1; -ms-grid-row: 2; background-color:red; } #middleColumn { -ms-grid-column: 2; -ms-grid-row: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; -ms-grid-row: 2; background-color:blue; } #footer { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 3; background-color: orange; } </style> </head> <body> <div id="container"> <div id="header"> Header, Header, Header </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="footer"> Footer, Footer, Footer </div> </div> </body> </html> In the page above, the grid layout is created with the following rule which creates a grid with three rows and three columns: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } The header is created with the following rule: #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } The header is positioned in column 1 and row 1. Furthermore, notice that the “-ms-grid-column-span” property is used to span the header across three columns. CSS Grid Layout and Fractional Units When you use CSS Grid Layout, you can take advantage of fractional units. Fractional units provide you with an easy way of dividing up remaining space in a page. Imagine, for example, that you want to create a three-column page layout. You want the size of the first column to be fixed at 200 pixels and you want to divide the remaining space among the remaining three columns. The width of the second column is equal to the combined width of the third and fourth columns. The following CSS rule creates four columns with the desired widths: #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } The fr unit represents a fraction. The grid above contains four columns. The second column is two times the size (2fr) of the third (1fr) and fourth (1fr) columns. When you use the fractional unit, the remaining space is divided up using fractional amounts. Notice that the single row is set to a height of 1fr. The single grid row gobbles up the entire vertical space. Here’s the entire HTML page: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } #firstColumn { -ms-grid-column: 1; background-color:red; } #secondColumn { -ms-grid-column: 2; background-color:green; } #thirdColumn { -ms-grid-column: 3; background-color:blue; } #fourthColumn { -ms-grid-column: 4; background-color:orange; } </style> </head> <body> <div id="container"> <div id="firstColumn"> First Column, First Column, First Column </div> <div id="secondColumn"> Second Column, Second Column, Second Column </div> <div id="thirdColumn"> Third Column, Third Column, Third Column </div> <div id="fourthColumn"> Fourth Column, Fourth Column, Fourth Column </div> </div> </body> </html>   Summary There is more in the CSS 3 Grid Layout standard than discussed in this blog post. My goal was to describe the basics. If you want to learn more than you can read through the entire standard at http://www.w3.org/TR/css3-grid-layout/ In this blog post, I described some of the difficulties that you might encounter when attempting to replace HTML tables with Cascading Style Sheets when laying out a web page. I explained how you can take advantage of the CSS 3 Grid Layout standard to avoid these problems when building Metro style applications using JavaScript. CSS 3 Grid Layout provides you with all of the benefits of using HTML tables for laying out a page without requiring you to use HTML table elements.

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • Adding Column to a SQL Server Table

    - by Dinesh Asanka
    Adding a column to a table is  common task for  DBAs. You can add a column to a table which is a nullable column or which has default values. But are these two operations are similar internally and which method is optimal? Let us start this with an example. I created a database and a table using following script: USE master Go --Drop Database if exists IF EXISTS (SELECT 1 FROM SYS.databases WHERE name = 'AddColumn') DROP DATABASE AddColumn --Create the database CREATE DATABASE AddColumn GO USE AddColumn GO --Drop the table if exists IF EXISTS ( SELECT 1 FROM sys.tables WHERE Name = 'ExistingTable') DROP TABLE ExistingTable GO --Create the table CREATE TABLE ExistingTable (ID BIGINT IDENTITY(1,1) PRIMARY KEY CLUSTERED, DateTime1 DATETIME DEFAULT GETDATE(), DateTime2 DATETIME DEFAULT GETDATE(), DateTime3 DATETIME DEFAULT GETDATE(), DateTime4 DATETIME DEFAULT GETDATE(), Gendar CHAR(1) DEFAULT 'M', STATUS1 CHAR(1) DEFAULT 'Y' ) GO -- Insert 100,000 records with defaults records INSERT INTO ExistingTable DEFAULT VALUES GO 100000 Before adding a Column Before adding a column let us look at some of the details of the database. DBCC IND (AddColumn,ExistingTable,1) By running the above query, you will see 637 pages for the created table. Adding a Column You can add a column to the table with following statement. ALTER TABLE ExistingTable Add NewColumn INT NULL Above will add a column with a null value for the existing records. Alternatively you could add a column with default values. ALTER TABLE ExistingTable Add NewColumn INT NOT NULL DEFAULT 1 The above statement will add a column with a 1 value to the existing records. In the below table I measured the performance difference between above two statements. Parameter Nullable Column Default Value CPU 31 702 Duration 129 ms 6653 ms Reads 38 116,397 Writes 6 1329 Row Count 0 100000 If you look at the RowCount parameter, you can clearly see the difference. Though column is added in the first case, none of the rows are affected while in the second case all the rows are updated. That is the reason, why it has taken more duration and CPU to add column with Default value. We can verify this by several methods. Number of Pages The number of data pages can be obtained by using DBCC IND command. Though, this an undocumented dbcc command, many experts are ok to use this command in production. However, since there is no official word from Microsoft, use this “at your own risk”. DBCC IND (AddColumn,ExistingTable,1) Before Adding the Columns 637 Adding a Column with NULL 637 Adding a column with DEFAULT value 1270 This clearly shows that pages are physically modified. Please note, a high value indicated in the Adding a column with DEFAULT value  column is also a result of page splits. Continues…

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  • Setting the comment of a column to that of another column in Postgresql

    - by dland
    Suppose I create a table in Postgresql with a comment on a column: create table t1 ( c1 varchar(10) ); comment on column t1.c1 is 'foo'; Some time later, I decide to add another column: alter table t1 add column c2 varchar(20); I want to look up the comment contents of the first column, and associate with the new column: select comment_text from (what?) where table_name = 't1' and column_name = 'c1' The (what?) is going to be a system table, but after having looked around in pgAdmin and searching on the web I haven't learnt its name. Ideally I'd like to be able to: comment on column t1.c1 is (select ...); but I have a feeling that's stretching things a bit far. Thanks for any ideas. Update: based on the suggestions I received here, I wound up writing a program to automate the task of transferring comments, as part of a larger process of changing the datatype of a Postgresql column. You can read about that on my blog.

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  • Getting started with Oracle Database In-Memory Part III - Querying The IM Column Store

    - by Maria Colgan
    In my previous blog posts, I described how to install, enable, and populate the In-Memory column store (IM column store). This weeks post focuses on how data is accessed within the IM column store. Let’s take a simple query “What is the most expensive air-mail order we have received to date?” SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE  lo_shipmode = 5; The LINEORDER table has been populated into the IM column store and since we have no alternative access paths (indexes or views) the execution plan for this query is a full table scan of the LINEORDER table. You will notice that the execution plan has a new set of keywords “IN MEMORY" in the access method description in the Operation column. These keywords indicate that the LINEORDER table has been marked for INMEMORY and we may use the IM column store in this query. What do I mean by “may use”? There are a small number of cases were we won’t use the IM column store even though the object has been marked INMEMORY. This is similar to how the keyword STORAGE is used on Exadata environments. You can confirm that the IM column store was actually used by examining the session level statistics, but more on that later. For now let's focus on how the data is accessed in the IM column store and why it’s faster to access the data in the new column format, for analytical queries, rather than the buffer cache. There are four main reasons why accessing the data in the IM column store is more efficient. 1. Access only the column data needed The IM column store only has to scan two columns – lo_shipmode and lo_ordtotalprice – to execute this query while the traditional row store or buffer cache has to scan all of the columns in each row of the LINEORDER table until it reaches both the lo_shipmode and the lo_ordtotalprice column. 2. Scan and filter data in it's compressed format When data is populated into the IM column it is automatically compressed using a new set of compression algorithms that allow WHERE clause predicates to be applied against the compressed formats. This means the volume of data scanned in the IM column store for our query will be far less than the same query in the buffer cache where it will scan the data in its uncompressed form, which could be 20X larger. 3. Prune out any unnecessary data within each column The fastest read you can execute is the read you don’t do. In the IM column store a further reduction in the amount of data accessed is possible due to the In-Memory Storage Indexes(IM storage indexes) that are automatically created and maintained on each of the columns in the IM column store. IM storage indexes allow data pruning to occur based on the filter predicates supplied in a SQL statement. An IM storage index keeps track of minimum and maximum values for each column in each of the In-Memory Compression Unit (IMCU). In our query the WHERE clause predicate is on the lo_shipmode column. The IM storage index on the lo_shipdate column is examined to determine if our specified column value 5 exist in any IMCU by comparing the value 5 to the minimum and maximum values maintained in the Storage Index. If the value 5 is outside the minimum and maximum range for an IMCU, the scan of that IMCU is avoided. For the IMCUs where the value 5 does fall within the min, max range, an additional level of data pruning is possible via the metadata dictionary created when dictionary-based compression is used on IMCU. The dictionary contains a list of the unique column values within the IMCU. Since we have an equality predicate we can easily determine if 5 is one of the distinct column values or not. The combination of the IM storage index and dictionary based pruning, enables us to only scan the necessary IMCUs. 4. Use SIMD to apply filter predicates For the IMCU that need to be scanned Oracle takes advantage of SIMD vector processing (Single Instruction processing Multiple Data values). Instead of evaluating each entry in the column one at a time, SIMD vector processing allows a set of column values to be evaluated together in a single CPU instruction. The column format used in the IM column store has been specifically designed to maximize the number of column entries that can be loaded into the vector registers on the CPU and evaluated in a single CPU instruction. SIMD vector processing enables the Oracle Database In-Memory to scan billion of rows per second per core versus the millions of rows per second per core scan rate that can be achieved in the buffer cache. I mentioned earlier in this post that in order to confirm the IM column store was used; we need to examine the session level statistics. You can monitor the session level statistics by querying the performance views v$mystat and v$statname. All of the statistics related to the In-Memory Column Store begin with IM. You can see the full list of these statistics by typing: display_name format a30 SELECT display_name FROM v$statname WHERE  display_name LIKE 'IM%'; If we check the session statistics after we execute our query the results would be as follow; SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE lo_shipmode = 5; SELECT display_name FROM v$statname WHERE  display_name IN ('IM scan CUs columns accessed',                        'IM scan segments minmax eligible',                        'IM scan CUs pruned'); As you can see, only 2 IMCUs were accessed during the scan as the majority of the IMCUs (44) in the LINEORDER table were pruned out thanks to the storage index on the lo_shipmode column. In next weeks post I will describe how you can control which queries use the IM column store and which don't. +Maria Colgan

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  • Change column height as other column gets longer

    - by Infiniti Fizz
    Hi, I have tried a few things to solve this problem but I can't seem to get it working. The problem is that I have 2 columns as the main part of my website, right and left. On some pages, there is a lot of text in the left column, therefore it is very long, the problem is that the right column doesn't elongate with the left column. Both columns have the same background colour and a footer s displayed across the width of both columns after the columns finish. My first thought was to put both columns inside a div which would have the same background colour as them and therefore if the left column became 1500px long in total and the right column stayed at around 600px (due to the elements inside it) then this wouldn't show as the new, outer div would elongate along with the left column. But for some reason this didn't work. Could it be because the columns are floated? Does anyone have any other ideas? Here is the website (Obviously not finished yet): Beansheaf Hotel I have chosen a page where there is a lot of text in the left column so the problem is apparent. Thanks in advance, InfinitiFizz

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  • SQL SERVER – Wait Stats – Wait Types – Wait Queues – Day 0 of 28

    - by pinaldave
    This blog post will have running account of the all the blog post I will be doing in this month related to SQL Server Wait Types and Wait Queues. SQL SERVER – Introduction to Wait Stats and Wait Types – Wait Type – Day 1 of 28 SQL SERVER – Signal Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28 SQL SERVER – DMV – sys.dm_os_wait_stats Explanation – Wait Type – Day 3 of 28 SQL SERVER – DMV – sys.dm_os_waiting_tasks and sys.dm_exec_requests – Wait Type – Day 4 of 28 SQL SERVER – Capturing Wait Types and Wait Stats Information at Interval – Wait Type – Day 5 of 28 SQL SERVER – CXPACKET – Parallelism – Usual Solution – Wait Type – Day 6 of 28 SQL SERVER – CXPACKET – Parallelism – Advanced Solution – Wait Type – Day 7 of 28 SQL SERVER – SOS_SCHEDULER_YIELD – Wait Type – Day 8 of 28 SQL SERVER – PAGEIOLATCH_DT, PAGEIOLATCH_EX, PAGEIOLATCH_KP, PAGEIOLATCH_SH, PAGEIOLATCH_UP – Wait Type – Day 9 of 28 SQL SERVER – IO_COMPLETION – Wait Type – Day 10 of 28 SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28 SQL SERVER – PAGELATCH_DT, PAGELATCH_EX, PAGELATCH_KP, PAGELATCH_SH, PAGELATCH_UP – Wait Type – Day 12 of 28 SQL SERVER – FT_IFTS_SCHEDULER_IDLE_WAIT – Full Text – Wait Type – Day 13 of 28 SQL SERVER – BACKUPIO, BACKUPBUFFER – Wait Type – Day 14 of 28 SQL SERVER – LCK_M_XXX – Wait Type – Day 15 of 28 Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL SERVER – Wait Stats – Wait Types – Wait Queues – Day 0 of 28

    - by pinaldave
    This blog post will have running account of the all the blog post I will be doing in this month related to SQL Server Wait Types and Wait Queues. SQL SERVER – Introduction to Wait Stats and Wait Types – Wait Type – Day 1 of 28 SQL SERVER – Single Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28 Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Test Column exists, Add Column, and Update Column

    - by david.clarke
    I'm trying to write a SQL Server database update script. I want to test for the existence of a column in a table, then if it doesn't exist add the column with a default value, and finally update that column based on the current value of a different column in the same table. I want this script to be runnable multiple times, the first time updating the table and on subsequent runs the script should be ignored. My script currently looks like the following: IF NOT EXISTS(SELECT * FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = 'PurchaseOrder' AND COLUMN_NAME = 'IsDownloadable') BEGIN ALTER TABLE [dbo].[PurchaseOrder] ADD [IsDownloadable] bit NOT NULL DEFAULT 0 UPDATE [dbo].[PurchaseOrder] SET [IsDownloadable] = 1 WHERE [Ref] IS NOT NULL END SQL Server returns error "Invalid column name 'IsDownloadable'", i.e. I need to commit the DDL before I can update the column. I've tried various permutations but I'm getting nowhere fast.

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  • SQL SERVER – Guest Post – Jacob Sebastian – Filestream – Wait Types – Wait Queues – Day 22 of 28

    - by pinaldave
    Jacob Sebastian is a SQL Server MVP, Author, Speaker and Trainer. Jacob is one of the top rated expert community. Jacob wrote the book The Art of XSD – SQL Server XML Schema Collections and wrote the XML Chapter in SQL Server 2008 Bible. See his Blog | Profile. He is currently researching on the subject of Filestream and have submitted this interesting article on the very subject. What is FILESTREAM? FILESTREAM is a new feature introduced in SQL Server 2008 which provides an efficient storage and management option for BLOB data. Many applications that deal with BLOB data today stores them in the file system and stores the path to the file in the relational tables. Storing BLOB data in the file system is more efficient that storing them in the database. However, this brings up a few disadvantages as well. When the BLOB data is stored in the file system, it is hard to ensure transactional consistency between the file system data and relational data. Some applications store the BLOB data within the database to overcome the limitations mentioned earlier. This approach ensures transactional consistency between the relational data and BLOB data, but is very bad in terms of performance. FILESTREAM combines the benefits of both approaches mentioned above without the disadvantages we examined. FILESTREAM stores the BLOB data in the file system (thus takes advantage of the IO Streaming capabilities of NTFS) and ensures transactional consistency between the BLOB data in the file system and the relational data in the database. For more information on the FILESTREAM feature, visit: http://beyondrelational.com/filestream/default.aspx FILESTREAM Wait Types Since this series is on the different SQL Server wait types, let us take a look at the various wait types that are related to the FILESTREAM feature. FS_FC_RWLOCK This wait type is generated by FILESTREAM Garbage Collector. This occurs when Garbage collection is disabled prior to a backup/restore operation or when a garbage collection cycle is being executed. FS_GARBAGE_COLLECTOR_SHUTDOWN This wait type occurs when during the cleanup process of a garbage collection cycle. It indicates that that garbage collector is waiting for the cleanup tasks to be completed. FS_HEADER_RWLOCK This wait type indicates that the process is waiting for obtaining access to the FILESTREAM header file for read or write operation. The FILESTREAM header is a disk file located in the FILESTREAM data container and is named “filestream.hdr”. FS_LOGTRUNC_RWLOCK This wait type indicates that the process is trying to perform a FILESTREAM log truncation related operation. It can be either a log truncate operation or to disable log truncation prior to a backup or restore operation. FSA_FORCE_OWN_XACT This wait type occurs when a FILESTREAM file I/O operation needs to bind to the associated transaction, but the transaction is currently owned by another session. FSAGENT This wait type occurs when a FILESTREAM file I/O operation is waiting for a FILESTREAM agent resource that is being used by another file I/O operation. FSTR_CONFIG_MUTEX This wait type occurs when there is a wait for another FILESTREAM feature reconfiguration to be completed. FSTR_CONFIG_RWLOCK This wait type occurs when there is a wait to serialize access to the FILESTREAM configuration parameters. Waits and Performance System waits has got a direct relationship with the overall performance. In most cases, when waits increase the performance degrades. SQL Server documentation does not say much about how we can reduce these waits. However, following the FILESTREAM best practices will help you to improve the overall performance and reduce the wait types to a good extend. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology Tagged: Filestream

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  • SQL Server: One large persisted computed column for Fulltext Indexing

    - by Alex
    It appears to me as the easiest, most straightforward solution, but please correct me if I'm wrong. Instead of having a fulltext index on all individual columns of a table, isn't it better to just generate one single wide computed column and run the fulltext index against that only? It appears to me that it gets rid of all the issues of having multiple columns, incl. that I can't search "x AND y" as this will not match a row with "x" present in column 1 and "y" present in column 2. Any counterarguments?

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  • SQLAuthority News – Online Webcast How to Identify Resource Bottlenecks – Wait Types and Queues

    - by pinaldave
    As all of you know I have been working a recently on the subject SQL Server Wait Statistics, the reason is since I have published book on this subject SQL Wait Stats Joes 2 Pros: SQL Performance Tuning Techniques Using Wait Statistics, Types & Queues [Amazon] | [Flipkart] | [Kindle], lots of question and answers I am encountering. When I was writing the book, I kept version 1 of the book in front of me. I wanted to write something which one can use right away. I wanted to create an primer for everybody who have not explored wait stats method of performance tuning. Well, the books have been very well received and in fact we ran out of huge stock 2 times in India so far and once in USA during SQLPASS. I have received so many questions on this subject that I feel I can write one more book of the same size. I have been asked if I can create videos which can go along with this book. Personally I am working with SQL Server 2012 CTP3 and there are so many new wait types, I feel the subject of wait stats is going to be very very crucial in next version of SQL Server. If you have not started learning about this subject, I suggest you at least start exploring this right now. Learn how to begin on this subject atleast as when the next version comes in, you know how to read DMVs. I will be presenting on the same subject of performance tuning by wait stats in webcast embarcadero SQL Server Community Webinar. Here are few topics which we will be covering during the webinar. Beginning with SQL Wait Stats Understanding various aspect of SQL Wait Stats Understanding Query Life Cycle Identifying three TOP wait Stats Resolution of the common 3 wait types and queues Details of the webcast: How to Identify Resource Bottlenecks – Wait Types and Queues Date and Time: Wednesday, November 2, 11:00 AM PDT Registration Link I thank embarcadero for organizing opportunity for me to share my experience on subject of wait stats and connecting me with community to further take this subject to next level. One more interesting thing, I will ask one question at the end of the webinar and I will be giving away 5 copy of my SQL Wait Stats print book to first five correct answers. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • WPF - DataGrid Column's ToolTip visibility based on the column's data length

    - by S.C.Vidhya
    In my application, i have tried to implement the visibility of tooltip based on the dataGrid Column's text length by using a converter. I am facing some problems in displaying the toolTip text. In the ToolTip, TextBlock's text binding is not working. If its binded with some hard coded strings, it works fine. Here below is the code that i have added for the grid column... <Custom:DataGridTemplateColumn.CellTemplate> <DataTemplate> <TextBlock Text="{Binding Text}"> <TextBlock.ToolTip> <ToolTip DataContext="{Binding Path=PlacementTarget, RelativeSource={x:Static RelativeSource.Self}}" Visibility="{Binding Converter={StaticResource ToolTipVis}}"> <TextBlock Text="{Binding Text}"> </ToolTip> </TextBlock.ToolTip> </TextBlock> </DataTemplate> </Custom:DataGridTemplateColumn.CellTemplate>

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  • SQL SERVER – Introduction to Wait Stats and Wait Types – Wait Type – Day 1 of 28

    - by pinaldave
    I have been working a lot on Wait Stats and Wait Types recently. Last Year, I requested blog readers to send me their respective server’s wait stats. I appreciate their kind response as I have received  Wait stats from my readers. I took each of the results and carefully analyzed them. I provided necessary feedback to the person who sent me his wait stats and wait types. Based on the feedbacks I got, many of the readers have tuned their server. After a while I got further feedbacks on my recommendations and again, I collected wait stats. I recorded the wait stats and my recommendations and did further research. At some point at time, there were more than 10 different round trips of the recommendations and suggestions. Finally, after six month of working my hands on performance tuning, I have collected some real world wisdom because of this. Now I plan to share my findings with all of you over here. Before anything else, please note that all of these are based on my personal observations and opinions. They may or may not match the theory available at other places. Some of the suggestions may not match your situation. Remember, every server is different and consequently, there is more than one solution to a particular problem. However, this series is written with kept wait stats in mind. While I was working on various performance tuning consultations, I did many more things than just tuning wait stats. Today we will discuss how to capture the wait stats. I use the script diagnostic script created by my friend and SQL Server Expert Glenn Berry to collect wait stats. Here is the script to collect the wait stats: -- Isolate top waits for server instance since last restart or statistics clear WITH Waits AS (SELECT wait_type, wait_time_ms / 1000. AS wait_time_s, 100. * wait_time_ms / SUM(wait_time_ms) OVER() AS pct, ROW_NUMBER() OVER(ORDER BY wait_time_ms DESC) AS rn FROM sys.dm_os_wait_stats WHERE wait_type NOT IN ('CLR_SEMAPHORE','LAZYWRITER_SLEEP','RESOURCE_QUEUE','SLEEP_TASK' ,'SLEEP_SYSTEMTASK','SQLTRACE_BUFFER_FLUSH','WAITFOR', 'LOGMGR_QUEUE','CHECKPOINT_QUEUE' ,'REQUEST_FOR_DEADLOCK_SEARCH','XE_TIMER_EVENT','BROKER_TO_FLUSH','BROKER_TASK_STOP','CLR_MANUAL_EVENT' ,'CLR_AUTO_EVENT','DISPATCHER_QUEUE_SEMAPHORE', 'FT_IFTS_SCHEDULER_IDLE_WAIT' ,'XE_DISPATCHER_WAIT', 'XE_DISPATCHER_JOIN', 'SQLTRACE_INCREMENTAL_FLUSH_SLEEP')) SELECT W1.wait_type, CAST(W1.wait_time_s AS DECIMAL(12, 2)) AS wait_time_s, CAST(W1.pct AS DECIMAL(12, 2)) AS pct, CAST(SUM(W2.pct) AS DECIMAL(12, 2)) AS running_pct FROM Waits AS W1 INNER JOIN Waits AS W2 ON W2.rn <= W1.rn GROUP BY W1.rn, W1.wait_type, W1.wait_time_s, W1.pct HAVING SUM(W2.pct) - W1.pct < 99 OPTION (RECOMPILE); -- percentage threshold GO This script uses Dynamic Management View sys.dm_os_wait_stats to collect the wait stats. It omits the system-related wait stats which are not useful to diagnose performance-related bottleneck. Additionally, not OPTION (RECOMPILE) at the end of the DMV will ensure that every time the query runs, it retrieves new data and not the cached data. This dynamic management view collects all the information since the time when the SQL Server services have been restarted. You can also manually clear the wait stats using the following command: DBCC SQLPERF('sys.dm_os_wait_stats', CLEAR); Once the wait stats are collected, we can start analysis them and try to see what is causing any particular wait stats to achieve higher percentages than the others. Many waits stats are related to one another. When the CPU pressure is high, all the CPU-related wait stats show up on top. But when that is fixed, all the wait stats related to the CPU start showing reasonable percentages. It is difficult to have a sure solution, but there are good indications and good suggestions on how to solve this. I will keep this blog post updated as I will post more details about wait stats and how I reduce them. The reference to Book On Line is over here. Of course, I have selected February to run this Wait Stats series. I am already cheating by having the smallest month to run this series. :) Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Types issue in F#

    - by Andry
    Hello! In my ongoing adventure deep diving into f# I am understanding a lot of this powerful language but there are things that I still do not understand so clearly. One of the most important issues I need to master is types. Well the book I am reading is very straight forward and introduces entities and main functionalities with a direct approach. The first thing I could get start with is types. It introduces the main types as list, option, tuples, and so on... It is clearly underlined that all these types are IMMUTABLE for many reasons regarding functional programming and data consistance in functional programing. Well, no problems until now... But now I am getting started with Concrete Types... Well... I have problems in managing with types like list, option, tuples, types created through new operator and concrete types created using type keyword (for abbreviations, concrete types...). So my question is: how can I efficently catalogue/distinguish all types of data in f#???? I can create a perfect separation among types in C#, VB.NET... FOr example in VB.NET there are value and reference types while in C# there are only references and also int, double are treated as objects (they are objects while in VB.NET a value type is not a object and there is a split in types for this reason). Well in F# I cannot create such differences among types in the language. Can you help me? I hope I was clear.

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  • Update table.column with another table.column with common joined column

    - by Matt
    Hit a speed bump, trying to update some column values in my table from another table. This is what is supposed to happen when everything works Correct all the city, state entries in tblWADonations by creating an update statement that moves the zip city from the joined city/state zip field to the tblWADonations city state TBL NAME | COLUMN NAMES tblZipcodes with zip,city,State tblWADonations with zip,oldcity,oldstate This is what I have so far: UPDATE tblWADonations SET oldCity = tblZipCodes.city, oldState = tblZipCodes.state FROM tblWADonations INNER JOIN tblZipCodes ON tblWADonations.zip = tblZipCodes.zip Where oldCity <> tblZipcodes.city; There seems to be easy ways to do this online but I am overlooking something. Tried this by hand and in editor this is what it kicks back. Msg 8152, Level 16, State 2, Line 1 String or binary data would be truncated. The statement has been terminated. Please include a sql statement or where I need to make the edit so I can mark this post as a reference in my favorites. Thanks!

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  • computing hash values, integral types versus struct/class

    - by aaa
    hello I would like to know if there is a difference in speed between computing hash value (for example std::map key) of primitive integral type, such as int64_t and pod type, for example struct { int16_t v[4]; };. I know this is going to implementation specific, so my question ultimately pertains to gnu standard library. Thanks

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  • C# find a value from an object column using a string from another column

    - by Graham
    I have 2 list in foreach loops.. I currently use a 'switch' statement on the m.columnname and then assign the value of that column to another var map as per below.. If m.columnname = 'DocHeading' then v.docheading is assigned to map.value.. There will always be a match on m.columnname to a column in the var v. is there a way to get the value from the var v using the string from m.columnname? The reason is that the users will want to add and change column names and I dont want to have to change this code all the time.. Hope this makes sense List spreadMapping = new List(); foreach (var m in mappings) { foreach (var v in hvalues) { SpreadMappings map = new SpreadMappings(); switch (m.ColumnName) { case “DocHeading”: map.ColumnX = m.ColumnX; map.ColumnY = m.ColumnY; map.ColumnValue = v.DocHeading; map.ColumnName = m.ColumnName; map.ColumnId = v.Id; map.ColumnSheetName = sheetName; spreadMapping.Add(map); break;

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  • php foreach question

    - by user295189
    I have the following code $oldID=-1; $column=0; foreach($pv->rawData as $data){ if ($oldID!= $data->relativeTypeID){ $oldID= $data->relativeTypeID; $column++; $row=1; } echo "Row: ".$row.": Column: ".$column.": ID".$data->relativeTypeID."<br>"; //if exists a description if($data->description){ //insert here in the array $pv->results[$data->relativeTypeID][$row][0]= $data->relation; $pv->results[$data->relativeTypeID][$row][1]= ''; $pv->results[$data->relativeTypeID][$row][2] =''; $pv->results[$data->relativeTypeID][$row][3] = ''; $row++; } } this generates this output Row: 1: Column: 1: ID1 Row: 2: Column: 1: ID1 Row: 1: Column: 2: ID2 Row: 2: Column: 2: ID2 Row: 3: Column: 2: ID2 Row: 4: Column: 2: ID2 Row: 5: Column: 2: ID2 Row: 6: Column: 2: ID2 Row: 7: Column: 2: ID2 Row: 8: Column: 2: ID2 Row: 9: Column: 2: ID2 Row: 10: Column: 2: ID2 Row: 11: Column: 2: ID2 Row: 1: Column: 3: ID3 Row: 1: Column: 4: ID4 Row: 1: Column: 5: ID8 Row: 2: Column: 5: ID8 Row: 3: Column: 5: ID8 Row: 1: Column: 6: ID10 Row: 2: Column: 6: ID10 Row: 3: Column: 6: ID10 Row: 4: Column: 6: ID10 ... .. what I want it to do is to stop at the top 4 columns so I want an output like this Row: 1: Column: 1: ID1 Row: 2: Column: 1: ID1 Row: 1: Column: 2: ID2 Row: 2: Column: 2: ID2 Row: 3: Column: 2: ID2 Row: 4: Column: 2: ID2 Row: 5: Column: 2: ID2 Row: 6: Column: 2: ID2 Row: 7: Column: 2: ID2 Row: 8: Column: 2: ID2 Row: 9: Column: 2: ID2 Row: 10: Column: 2: ID2 Row: 11: Column: 2: ID2 Row: 1: Column: 3: ID3 Row: 1: Column: 4: ID4 as you can see it stopped at column 4. Thanks

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